Efficient Mechanism For Privacy And Improve The Quality Answers In Q&A Systems

Question and Answer (Q&A) systems piece a dynamic role in our daily life for evidence and data sharing. Users post questions and pick questions to rejoinder in the system. Due to the hastilybudding user population and the number of questions, it is questionable for a user to stagger upon a request by unplanned that (s)he can answer. Also, selflessness does not embolden all users to afford answers, not to mention high quality rejoinders with a short answer wait time. The principalunprejudiced of this paper is to increase the performance of Q&A systems by dynamicallyaccelerating questions to users who are gifted and disposed to answer the questions. Our results submit that social networks can be leveraged to recover the response quality and asker’s waiting time. We also applied a real prototype of SocialQ&A, and examine the Q&A conduct of real users and queries from a small-scale real-world SocialQ&A system

Respons Hematopoitik Mencit yang Diinfeksi dengan Plasmodium Berghei Stadium Eritrositik Iradiasi Gamma

RESPONS HEMATOPOITIK MENCIT YANG DIINFEKSI DENGAN Plasmodiumberghei STADIUM ERITROSITIK IRADIASI GAMMA. Salah satu strategi untuk mengontrolpenyakit malaria adalah pengembangan vaksin melalui pelemahan parasit Plasmodium bergheidengan iradiasi gamma. Pada penelitian ini, P. berghei yang diiradiasi gamma dan yang tidakdiiradiasi disuntikkan secara peritoneal pada mencit untuk mempelajari respons hematopoitik.Respons hematopoitik ditentukan melalui persen parasitemia, jumlah eritrosit, leukosit, limfosit,dan monosit setiap 2 hari sekali selama 14 hari. Berat organ limpa dan hati mencit diukursetiap 3 hari sesudah infeksi. Mencit yang diinfeksi dengan parasit yang diiradiasi memilikiperiode prepaten 5 hari dengan parasitemia rendah dan jumlah eritrosit mengalami sedikitpenurunan. Jumlah leukosit naik hampir dua kali dari konsentrasi awal, dan jumlah limfosit sertamonosit juga mengalami kenaikan. Mencit yang diinfeksi dengan P. berghei tanpa iradiasimemiliki periode prepaten 2 hari dengan parasitemia meningkat, jumlah eritrosit mengalamipenurunan hingga 75%, dan jumlah leukosit tidak mengalami peningkatan. Limpa maupun hatimencit yang diinfeksi dengan P. berghei yang diiradiasi mengalami sedikit kenaikan berat,sedangkan pada mencit yang diinfeksi dengan P. berghei tanpa iradiasi terjadi kenaikan yangsignifikan. Peningkatan jumlah leukosit, limfosit, monosit, dan rendahnya parasitemia padamencit yang diinfeksi P. berghei yang diiradiasi menunjukkan terjadinya respons imun padamencit

FORMULATION AND EVALUATION OF RAMIPRIL MOUTH DISSOLVING FILMS

Objective: The present investigation was aimed at preparation and evaluation of mouth dissolving films (MDFs) of Ramipril to enhance patient convenience, compliance and to improve bioavailability. Methods: MDFs with 0.5% w/w Ramipril were prepared by a solvent casting method using a wet film applicator. The effects of film formers, wetting/solubilizing, saliva stimulating agents and film modifiers on the physicomechanical and in vitro Ramipril release from MDFs were evaluated. Results: The MDFs prepared were transparent, smooth and showed no re-crystallization upon storage. MDFs casted with hydroxypropyl methylcellulose (HPMC) E3 as film former and polyethylene glycol (PEG-400) as plasticizer showed superior Ramipril release rates and good physicomechanical properties when compared to MDFs with E5 and E15 as film formers. HPMC E3 MDFs with polyvinyl pyrrolidone K30 (PVP K30) and sodium lauryl sulphate (SLS) gave superior drug release properties than MDFs without PVP K30 and SLS. The HPMC E3 MDFs with citric acid (CA) as saliva stimulating and xylitol as soothing agent gave significantly superior in vitro drug release than the MDFs without CA and xylitol. Release kinetics data reveals diffusion as a drug release mechanism. Conclusion: From the obtained results, it can be concluded that the administration of Ramipril as MDF may provide a quick onset of action with enhanced oral bioavailability and therapeutic efficacy

SCREENING AND OPTIMIZATION OF VALACYCLOVIR NIOSOMES BY DESIGN OF EXPERIMENTS

Objective: The objective of the study was to perform a screening, optimization of valacyclovir niosomal formulation to achieve a sustained release of drug using the design of experiments by 32 full factorial design.Methods: Valacyclovir loaded niosomes were prepared using thin film hydration method by varying the ratio of Span 60 and Cholesterol. The prepared niosomes were evaluated for vesicle size, entrapment efficiency, cumulative drug release, fourier transformed infrared spectroscopy (FTIR), zeta potential and surface morphology by field emission scanning electron microscopy (FESEM).Results: The valacyclovir was successfully encapsulated and its entrapment efficiency ranged from 36.70 % to 50.62 %. The average vesicle size of the niosomes was found to be 431 to 623 nm. At 8th hour the drug release varied from 77.50% to 96.31 %. The optimized niosomes were multilamellar with a surface charge potential of about-43.2 mV. The studies revealed that the interaction of cholesterol and surfactant had a substantial effect on vesicle size, entrapment efficiency and drug release from the niosomes. The release kinetics of the optimized niosomes followed zero order kinetics with fickian diffusion controlled mechanism. The stability studies were performed for the optimized formulation and found that the formulation is stable at 4°C ± 2°C.Conclusion: Model equations were developed for the responses. No significant difference was observed between the predicted and observed value, showing that the developed model is reliable

A Tri-Band Heart Shaped Microstrip Patch Antenna

In this paper a tri-band heart shaped Microstrip patch antenna is presented for Bluetooth and C-band applications. The proposed antenna has symmetrical properties and has been designed by etching heart shaped structure on FR-4 substrate with coaxially fed input. It radiates for Bluetooth, WLAN and WIMAX Frequency bands. The gain at 2.4 GHz, 5.4 GHz, and 7.6 GHz respectively is obtained. The proposed antenna can be widely used for Bluetooth, WLAN applications. DOI: 10.17762/ijritcc2321-8169.15033

NeuroWrite: Predictive Handwritten Digit Classification using Deep Neural Networks

The rapid evolution of deep neural networks has revolutionized the field of machine learning, enabling remarkable advancements in various domains. In this article, we introduce NeuroWrite, a unique method for predicting the categorization of handwritten digits using deep neural networks. Our model exhibits outstanding accuracy in identifying and categorising handwritten digits by utilising the strength of convolutional neural networks (CNNs) and recurrent neural networks (RNNs).In this article, we give a thorough examination of the data preparation methods, network design, and training methods used in NeuroWrite. By implementing state-of-the-art techniques, we showcase how NeuroWrite can achieve high classification accuracy and robust generalization on handwritten digit datasets, such as MNIST. Furthermore, we explore the model's potential for real-world applications, including digit recognition in digitized documents, signature verification, and automated postal code recognition. NeuroWrite is a useful tool for computer vision and pattern recognition because of its performance and adaptability.The architecture, training procedure, and evaluation metrics of NeuroWrite are covered in detail in this study, illustrating how it can improve a number of applications that call for handwritten digit classification. The outcomes show that NeuroWrite is a promising method for raising the bar for deep neural network-based handwritten digit recognition.Comment: 6 pages, 10 figure

A LDSS-CP-ABE Algorithm to Migrate Major Computation Overhead from Mobile Devices on to Proxy Server

Cloud has hugequantity of resources. In such a situation, to attain the acceptable presentation, it is indispensable to usage the possessionsdelivered by the cloud service provider (CSP) to stock and segment the data. At the moment, many cloud mobile claims have been extensivelycastoff. In these claims, data owners can upload their photos, videos, documents and other files to the cloud and segment these data with other data users they like to stake.  Explanations with stumpy computational overhead are in prodigious need for mobile cloud applications. In this paper, we recommend a lightweight data sharing scheme (LDSS) for mobile cloud computing.  The investigational results show that LDSS can confirm data concealment in mobile cloud and decrease the overhead on users’ side in mobile cloud

Study on knowledge, attitude and practices with respect to antibiotic use among medical students

Background: Antibiotic resistance has emerged as a serious global problem. Irrational prescribing of antibiotics is one of the key factors responsible for the development of antibiotic resistance. As today’s medical students will be the future prescribers their awareness regarding antibiotic resistance and rational prescribing of antibiotics is very much important. The main objective of the study was to evaluate the knowledge, attitude and practices of the medical students towards antibiotic use.Methods: A cross-sectional questionnaire-based study was conducted involving the medical students from all the semesters. A predesigned questionnaire was used to evaluate the knowledge, attitude and practices of the respondents. The response options were ‘yes’ or ‘no’ for some questions, and a 5 point Likert scale used to assess some questions. The data was analyzed using MS office Excel.Results: A total of 457 fully completed questionnaires from the respondents were evaluated. Majority of the respondents (70%) were females, and 91% of the respondents were aware of antibiotic resistance as a global problem. On analysis of score of questions assessing knowledge, 91% of respondents had attained a score in the range of 5-7. Majority (83%) of the respondents used antibiotics only on doctor’s prescription and 74% of the respondents had completed the prescribed course.Conclusions: The present study gives an insight on the knowledge, attitude and practices of medical students on antibiotic use, and can be used as a tool to plan educational strategies to improve the knowledge of the students regarding rational use of antibiotics

American Anthem: Modern American History

This Teacher\u27s Edition of American Anthem: Modern American History covers the United States Before 1898 and with emphasis on the nation becoming a world power, the First World War, from war to peace, the roaring twenties, the Great Depression, the New Deal, World War II, the Cold War, post-war America, the Civil Rights Movement, the Vietnam War, a conservative era, and into the twenty-first century.https://scholarship.richmond.edu/bookshelf/1282/thumbnail.jp

Spatially autocorrelated training and validation samples inflate performance assessment of convolutional neural networks

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive modelling. However, the predictive performance of such models is commonly estimated using random cross-validation, which does not account for spatial autocorrelation between training and validation data. Independent of the analytical method, such spatial dependence will inevitably inflate the estimated model performance. This problem is ignored in most CNN-related studies and suggests a flaw in their validation procedure. Here, we demonstrate how neglecting spatial autocorrelation during cross-validation leads to an optimistic model performance assessment, using the example of a tree species segmentation problem in multiple, spatially distributed drone image acquisitions. We evaluated CNN-based predictions with test data sampled from 1) randomly sampled hold-outs and 2) spatially blocked hold-outs. Assuming that a block cross-validation provides a realistic model performance, a validation with randomly sampled holdouts overestimated the model performance by up to 28%. Smaller training sample size increased this optimism. Spatial autocorrelation among observations was significantly higher within than between different remote sensing acquisitions. Thus, model performance should be tested with spatial cross-validation strategies and multiple independent remote sensing acquisitions. Otherwise, the estimated performance of any geospatial deep learning method is likely to be overestimated